Vol. 30
Latest Volume
All Volumes
PIERB 106 [2024] PIERB 105 [2024] PIERB 104 [2024] PIERB 103 [2023] PIERB 102 [2023] PIERB 101 [2023] PIERB 100 [2023] PIERB 99 [2023] PIERB 98 [2023] PIERB 97 [2022] PIERB 96 [2022] PIERB 95 [2022] PIERB 94 [2021] PIERB 93 [2021] PIERB 92 [2021] PIERB 91 [2021] PIERB 90 [2021] PIERB 89 [2020] PIERB 88 [2020] PIERB 87 [2020] PIERB 86 [2020] PIERB 85 [2019] PIERB 84 [2019] PIERB 83 [2019] PIERB 82 [2018] PIERB 81 [2018] PIERB 80 [2018] PIERB 79 [2017] PIERB 78 [2017] PIERB 77 [2017] PIERB 76 [2017] PIERB 75 [2017] PIERB 74 [2017] PIERB 73 [2017] PIERB 72 [2017] PIERB 71 [2016] PIERB 70 [2016] PIERB 69 [2016] PIERB 68 [2016] PIERB 67 [2016] PIERB 66 [2016] PIERB 65 [2016] PIERB 64 [2015] PIERB 63 [2015] PIERB 62 [2015] PIERB 61 [2014] PIERB 60 [2014] PIERB 59 [2014] PIERB 58 [2014] PIERB 57 [2014] PIERB 56 [2013] PIERB 55 [2013] PIERB 54 [2013] PIERB 53 [2013] PIERB 52 [2013] PIERB 51 [2013] PIERB 50 [2013] PIERB 49 [2013] PIERB 48 [2013] PIERB 47 [2013] PIERB 46 [2013] PIERB 45 [2012] PIERB 44 [2012] PIERB 43 [2012] PIERB 42 [2012] PIERB 41 [2012] PIERB 40 [2012] PIERB 39 [2012] PIERB 38 [2012] PIERB 37 [2012] PIERB 36 [2012] PIERB 35 [2011] PIERB 34 [2011] PIERB 33 [2011] PIERB 32 [2011] PIERB 31 [2011] PIERB 30 [2011] PIERB 29 [2011] PIERB 28 [2011] PIERB 27 [2011] PIERB 26 [2010] PIERB 25 [2010] PIERB 24 [2010] PIERB 23 [2010] PIERB 22 [2010] PIERB 21 [2010] PIERB 20 [2010] PIERB 19 [2010] PIERB 18 [2009] PIERB 17 [2009] PIERB 16 [2009] PIERB 15 [2009] PIERB 14 [2009] PIERB 13 [2009] PIERB 12 [2009] PIERB 11 [2009] PIERB 10 [2008] PIERB 9 [2008] PIERB 8 [2008] PIERB 7 [2008] PIERB 6 [2008] PIERB 5 [2008] PIERB 4 [2008] PIERB 3 [2008] PIERB 2 [2008] PIERB 1 [2008]
2011-05-22
Medical Image Fusion Based on Ripplet Transform Type-I
By
Progress In Electromagnetics Research B, Vol. 30, 355-370, 2011
Abstract
The motivation behind fusing multimodality, multiresolution images is to create a single image with improved interpretability. In this paper, we propose a novel multimodality Medical Image Fusion (MIF) method, based on Ripplet Transform Type-I (RT) for spatially registered, multi-sensor, multi-resolution medical images. RT is a new Multi-scale Geometric Analysis (MGA) tool, capable of resolving two dimensional (2D) singularities and representing image edges more efficiently. The source medical images are first transformed by discrete RT (DRT). Different fusion rules are applied to the different subbands of the transformed images. Then inverse DRT (IDRT) is applied to the fused coefficients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis shows, that the proposed technique performs better compared to fusion scheme based on Contourlet Transform (CNT).
Citation
Sudeb Das, Manish Chowdhury, and Malay Kumar Kundu, "Medical Image Fusion Based on Ripplet Transform Type-I," Progress In Electromagnetics Research B, Vol. 30, 355-370, 2011.
doi:10.2528/PIERB11040601
References

1. Daneshvar, S. and H. Ghassemian, "MRI and PET image fusion by combining IHS and retina-inspired models," Information Fusion, Vol. 11, No. 2, 114-123, April 20 2010.
doi:10.1016/j.inffus.2009.05.003

2. Barra, V. and J. Y. Boire, "A general framework for the fusion of anatomical and functional medical images," NeuroImage, Vol. 13, No. 3, 410-424, March 2001.
doi:10.1006/nimg.2000.0707

3. Shivappa, S. T., B. D. Rao, and M. M. Trivedi, "An iterative decoding algorithm for fusion of multimodal information," EURASIP Journal on Advances in Signal Processing, Vol. 2008, 2008.

4. Li, S. and B. Yang, "Multifocus image fusion using region segmentation and spatial frequency," Proceedings of Image Vision Computing, Vol. 26, No. 7, 971-979, July 2008.
doi:10.1016/j.imavis.2007.10.012

5. Yonghong, J., "Fusion of landsat TM and SAR image based on principal component analysis," Remote Sensing Technology and Application, Vol. 13, No. 1, 46-49, March 1998.

6. Li, H., B. S. Manjunath, and S. K. Mitra, "Multisensor image fusion using the wavelet transform," Proceedings of CVGIP: Graphical Model and Image Processing, Vol. 57, No. 3, 235-245, May 1995.
doi:10.1006/gmip.1995.1022

7. Yang, Y., D. S. Park, S. Huang, and N. Rao, "Medical image fusion via an effective wavelet-based approach," EURASIP Journal on Advances in Signal Processing, Vol. 2010, 2010.

8. Amolins, K., Y. Zhang, and P. Dare, "Wavelet based image fusion techniques | An introduction, review and comparison," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 62, No. 4, 249-263, Sept. 2007.
doi:10.1016/j.isprsjprs.2007.05.009

9. Yanga, L., B. L. Guoa, and W. Ni, "Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform," Neurocomputing, Vol. 72, No. 1--3, 203-211, Dec. 2008.
doi:10.1016/j.neucom.2008.02.025

10. Ali, F. E., I. M. El-Dokany, A. A. Saad, and F. E. Abd El-Samie, "Curvelet fusion of MR and CT images," Progress In Electromagnetics Research C, Vol. 3, 215-224, 2008.
doi:10.2528/PIERC08041305

11. Xu, J., L. Yang, and D. Wu, "Ripplet: A new transform for image processing," Journal of Visual Communication and Image Representation , Vol. 21, No. 7, 627-639, Oct. 2010.
doi:10.1016/j.jvcir.2010.04.002

12. Starck, J. L., E. J. Candes, and D. L. Donoho, "The curvelet transform for image denoising," IEEE Transactions on Image Processing, Vol. 11, No. 6, 670-684, Jun. 2000.
doi:10.1109/TIP.2002.1014998

13. Shlens, J., "A tutorial on principal component analysis,".
doi:http://www.snl.salk.edu/»shlens/pca.pdf

14. Qu, G. H., D. L. Zhang, and P. F. Yan, "Information measure for performance of image fusion," Electronic Letters, Vol. 38, No. 7, 313-315, 2002.
doi:10.1049/el:20020212

15. Kumar, G. R. H. and D. Singh, "Quality assessment of fused image of modis and palsar," Progress In Electromagnetics Research B, Vol. 24, 191-221, 2010.
doi:10.2528/PIERB10072101